Maritimes regional application of the national framework for assessing the vulnerability of biological components to ship-source oil spills in the marine environment
Bibliographic record
Abstract
An important contribution to fulfilling Fisheries and Oceans Canada (DFO) commitment in oil– spill response planning was the development of a framework for the rapid assessment of vulnerability of marine biological components to ship-source oil spills that fall under the DFO mandate, contributing to the ecological aspects of the ‘Resources at Risk’ component of oil spill planning and response. A National framework, developed in 2017 (Thornborough et al. 2017) – uses a structured approach for assessing and screening biological components expected to be most affected by a ship-source oil spill, utilizing a suite of criteria to assess vulnerability. The framework identified two key phases for assessing vulnerabilities of marine components: 1. Grouping of biological components (sub-groups) based upon shared characteristics related to oil vulnerability; and 2. Scoring of biological sub-groups against ecological vulnerability criteria (Exposure, Sensitivity, and Recovery) to identify those most vulnerable to oil using a binary scoring system. For validation purposes, the National framework stressed the need to apply and test the framework in a variety of marine aquatic environments across Canada. This research document describes how the National framework was used in the Maritimes Region, to: 1. Adapt the National framework to create appropriate sub-groups for Maritimes Region biota; 2. Apply the National scoring criteria to Maritimes Region sub-groups, adapting scoring criteria where necessary; and 3. Develop a rank list of sub-groups most vulnerable to a ship-source oil spill in the Maritimes Region. The vulnerability results from the application of the National framework in the Maritimes Region will help identify marine sub-groups that are most vulnerable to oil and will be used to inform oil spill response strategies in an effort to manage and limit the impacts of oil spills in the Region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".